Support vector machine based exploratory projection pursuit optimization for user face identification

For most real-world biometric identification applications, the training database size could be very large, i.e. in the range of several thousands. This yields to the curse of dimensionality problem. The downside of such a problem is that it could negatively affect both the identification performance and speed. In this paper we use Exploratory Projection Pursuit (EPP) methods to determine clusters of users having significant similarities and then apply Support Vector Machine (SVM) classifiers on each cluster of users independently. This allows reducing the dimensionality of the dataset for training SVMs and thus improving the performance of user identification.